Asian Journal of Information Technology
ISSN: Online 1993-5994ISSN: Print 1682-3915
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Extracting Features from ECG and Respiratory Signals for Automatic Supervised Classification of Heartbeat Using Neural Networks
Benali Medjahed Oussama, Bachir M`Hamed Saadi and Hadj Slimane Zine-Eddine
Page: 53-59 | Received 21 Sep 2022, Published online: 21 Sep 2022
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Abstract
Electrocardiogram (ECG) is today one of the essential pillars of the diagnosis of heart problems. The analysis of this signal and the identification of its parameters is an important step for diagnosis. In this study, we present a new algorithm for ECG signal classification. Respiratory signal simultaneously recorded with the ECG signal will be used to classify each heart beat into two classes (abnormal and normal class) by the extraction of their parameters using various Multi-Layered Perceptron Neural Classifiers (MLPNNs). Principal Component Analysis (PCA) is used to reduce dimensions of input features and improve the performance of the neural classifiers. This algorithm is tested on Apnea-ECG database from the universal MIT PhysioNet. As it will be shown later, the proposed algorithm allows to achieve high classification performances, describes both by sensitivity, specificity and the rate of correct classification parameters.
How to cite this article:
Benali Medjahed Oussama, Bachir M`Hamed Saadi and Hadj Slimane Zine-Eddine. Extracting Features from ECG and Respiratory Signals for Automatic Supervised Classification of Heartbeat Using Neural Networks.
DOI: https://doi.org/10.36478/ajit.2015.53.59
URL: https://www.makhillpublications.co/view-article/1682-3915/ajit.2015.53.59